Transcript
Speaker: Welcome back to Policy Biz Podcast. I'm your host, John Schwabisch. On this week's episode of the show, we continue our journey through different types of data. This time, we're going to talk about local data and how all data are local and how we need to be more aware of Local data when we are creating our data analysis our dashboards our graphs our charts our diagrams whatever we are creating And so to help me better understand how data are local I am pleased to be joined by Yanni Lukisus from Georgia Tech University We first talk about Yanni's book.
Speaker: I'm gonna grab it over here All Data Are Local. This is an open source book, by the way. I'm holding it up on the video if you're watching, but you can also get this open source. It is an open book. You can go get it for free if you want, but you can also buy the lovely hardcover book.
Speaker: um I really enjoyed this book about thinking critically through data, how data are local, and we can use them in local contexts. And so then when I started researching Yanni and his work, I found this whole lab where he's actually doing hands-on, ah not just data physicalization, which if you know anything about policy biz, you know I'm very much into that, but also using those approaches with people, with communities, so that they can better understand their own community and help inform policymakers and stakeholders on how to maybe better provide services, or as you're going to hear in this interview, in in our discussion, mitigating effects of climate change, as you're going to hear in a little bit. So I think it's a really fun episode. I think you're going to enjoy it. I think you should also check out the book.
Speaker: Check out Yanni's website as well, which is linked on the show notes. And check all that out. Before you listen to the episode, do me a quick favor if you can. Rate, review the show. on iTunes, Spotify, wherever you get your podcasts, this show is there.
Speaker: Or if you're watching on YouTube, give me a thumbs up right there just so I can continue to share the show and get more and more guests to join me to talk about data, data visualization, data communication, presentation skills, and all the stuff that you enjoy every other week.
Speaker: All right. With all that said, No more delay from me. Let's get over to my interview with Yanni on this episode of the Policy Biz Podcast.
Speaker: Hey, Yanni, good to see you. Welcome to the show. Thanks so much for having me, John. It's great to be here. Very excited to chat with you. We've got some good stuff on our plate. We've already talked for 30 minutes before we recorded, which is always which is always a good sign. um So kind of two main areas I want to talk to you about. One is your book right here.
Speaker: I've got it. All data are local ah with all of my red tabs all over the place. and I don't think I put notes in here. I don't like writing in books. So i just use stickies. um And then I want to talk about some of your, your, of your current work. So um maybe just a ah quick start of, for folks ah who you are, where you are now. And then we could talk about like, we'll just start with the book. Like um what inspired you to write it and you know, what do you hope people get out of it?
Speaker: Yeah. So yeah, I'm a associate professor of digital media at Georgia tech. I'm, calling in from Atlanta, Georgia. I've been here for about 10 years.
Speaker: I teach within the ah College of Liberal Arts at Georgia Tech, Ivan Allen College. And in the we have a very interdisciplinary school, the School of Literature, Media, and Communication. So it's it's it's a bit of a ah eccentric grab bag of people from across the arts and humanities and design, um but um a very fun place to work.
Speaker: i I really started writing the book in earnest when I got here. ah i had been in Boston previously, and I had been working on data viz for for quite some time.
Speaker: But upon arriving in Atlanta, you know, with this new job, I really wanted to craft a ah larger scale project for myself.
Speaker: And this was, let's see, I arrived here in 2014. this was like the era of big data. ah you know yeah Everybody was talking about big data and and you don't hear that phrase so much these days. no it's right Of course, it's in everything we do with generative AI and so forth, but ah it was novel. And it was, you know we we we were seeing these streams of data at a scale that was unprecedented and people didn't know how to think about it.
Speaker: um And there was this sense that you know, big data, we're going to change everything, you know, education, research, government, business.
Speaker: ah And ah I was interested in finding some creative and critical ways to think about and work with big data. And the more I got into looking at big data sets and some of the early ones I looked at were like the Digital Public Library of America, which was like bringing together ah library and museum digitized collections from across the country into into one big kind of mega meta database, as it was called.
Speaker: ah That was a project that came out of the Harvard harvard Berkman Center. um And that that project actually informed a lot of my questions about big data because I was seeing...
Speaker: you know, these enormous collections, but they were aggregated from smaller heterogeneous collections, you know, that each had their own metadata and kind of local um idiosyncrasies. yeah And, you know, it was really seen as a problem by the originators of that project. Like how are we going to normalize all these data? So from like a single search bar, you could find anything.
Speaker: yeah And so I think pretty early on, you know I began to ask, like well, what if the heterogeneity is not seen as a problem? you know It's kind of an old old hat, I guess, to say, like it's a not a bug, it's a feature. But as I looked into some of the heterogeneous characteristics of these data,
Speaker: they were so interesting, so human and really spoke about like the places these data came from, whether it was like the local regional names for places like, you know, you'd see some records that it was produced in like the Midlands and you're like, well, where is that? yeah Or just like the way a date was written. I did this one project with, um,
Speaker: student where we we actually didn't even look at all the dates across the Digital Public Library of America, only from the New York Public Library's subsection. and And we found, ah I think it was around 1,800 ways of writing the date.
Speaker: in that one sub collection. And you know when you look closely at it, it's you know it makes sense. It's like, sometimes they just have the year, sometimes they have the year and the month, sometimes they have the day, sometimes they're written in different order, sometimes have Roman numerals or like the name of the printer or the publisher.
Speaker: ah And I began to see these as not just facts about objects, but cultural artifacts. So bringing that back to this issue of big data, you know, I said, well, what if we approached all data as being local? And and maybe some of them are more aggregated than others, but they're all coming from a place and a time from an organization usually kind of grounded in disciplinary ways of making data, specific instruments, um and the expectation that they're being looked at by specific audiences.
Speaker: yeah You know, not for a lot of these data, let's say in in in the libraries, they weren't expecting that patrons were going to be seeing the data. It was meant for the librarians to use. Or some of the collections like an archive is organized very differently from a library. And yet, you know, if you want to bring them together.
Speaker: and so... That began to kind of inform this larger inquiry. and And I looked at not just libraries, but I looked at um scientific data from an arboretum also tied to Harvard University. I'd been working there previously.
Speaker: And then when I got to Atlanta, I started to look at local Atlanta related things. You know, I got really interested in in journalism and how news can be dealt with as data. There are a lot of these kind of news aggregators. you know We're very used to seeing this now.
Speaker: um Using things like natural language processing, or um housing, housing data. you know That was a big a big part and ended up being the last piece of the book.
Speaker: um and i And that chapter really takes on Zillow as this kind of aggregator of housing data and kind of delving into how are the data that go into Zillow's estimates, how are they created differently in different places and why does that matter?
Speaker: Yeah. Yeah, it's it's interesting because I feel like a lot of us get our data, whatever project we're working on, and we kind of... ignore some of these subtleties and you know, how they were collected. was just, I'm just finishing a project where I'm merging different data sets, like at the different, at the different County levels. And just even that, which is supposed to be standardized.
Speaker: um There are different parts of the country that they are not standard or they're standardized, but they're standardized in a different way. Like Connecticut yeah has its own thing going on. And it's like, from a kind of productivity perspective,
Speaker: it's frustrating because you have to like carve out this whole other part to like deal with Connecticut and Massachusetts. But from a local perspective, um it It is the experience of people who are yeah living there.
Speaker: There is a ah quote in your book that I have ah written down um that I've included in a few other things I'm doing. and And the quote is this, and I wanted you to talk ah a bit about it. um In the book, you write, what if we learn to see data as situated and partial because of their place attachments?
Speaker: um which I think is just a great sort of almost summary what you were just talking about. But was wondering you talk a little bit more about about how you think about data in that local context and then how that has kind of spurred or encouraged your kind of current line of of research and thinking.
Speaker: Yeah. So I think it's important to say that this idea of place attachment or context and And also, you know, spatial context mattering or making a difference is not something that comes out of nowhere.
Speaker: I was actually originally trained as an architect. And even though i I left architecture a long time ago, i sometimes describe myself as a renegade architect, I carry with me some of these sensibilities.
Speaker: And i think something that was kind of drilled into me early on was this sense that architecture or good architecture is a response to the context and whether that context is, you know, the, um the site, you know, whether it's how the site is sloped or views that might be available or kind of where the sun will be coming, um where winds are coming from, um access, you know, so,
Speaker: The idea for me of like thinking about a design as independent of place was a kind of anathema. you know yeah and And as I started to work, you know I was very interested in data and and visualization, as a lot of architects have been. um But it kind of never sit well with me that this was supposed to be um place agnostic work.
Speaker: and And really this this idea that place doesn't matter goes back, it's kind of been aspirational, it goes back to people like Marshall McLuhan, who talked about electronic media collapsing space and time, or Nicholas Negroponte, who was actually an architect or is, you know, was trained but as an architect, you know, wrote...
Speaker: back in the 90s about ah it mattering less and less um where you are when you're using digital media. And I think this really came together not just as a discomfort, but a real problem um with with larger stakes. When I started to read work by Anita Chan, um she writes about the problems of what she calls digital universalism.
Speaker: um And that's the notion that it doesn't matter where you are, who you are, when you are, when you're using digital media, you're just a user. um And we're all the same.
Speaker: And ah I think in that sense, the stakes are quite high because it means that, you know, there could be potentially an erasure of that context um that could be harmful.
Speaker: Some people have talked about this as a kind of digital colonialism or data colonialism, a kind of extractive thing. And and I saw this very much in in some of these library projects where it's like, okay, we're going to take your data and then we're going to remake it. So it conforms to our needs, our expectations, rather than asking, you know, how did these collections work?
Speaker: mattered, you know, in the, in the context where they were formed, right what you know, and, um, how were they tracked? How were they organized and why does that matter for understanding them?
Speaker: yeah Um, and so, you know, the, in the most extreme cases, it can be pretty, pretty daunting. I mean, some of these local, um, conditions can be, you know, they seem like frivolous, right?
Speaker: but um But other times, you know, it really hits you. i i remember i was having this discussion with them ah former curator at the Smithsonian, Marta McWhorter.
Speaker: And she said she had done this search in the collection for, you know, she was she was like, what if I type in black in the Smithsonian, you know, search query bar?
Speaker: And she got ah all these artifacts, art, objects related to black culture in various ways. oh And then she typed in, she's like, okay, well, let me search for white.
Speaker: And, you know, of course, white culture is not something that is like tracked and cataloged in a collection like that.
Speaker: um and And why? And she said, well, because it's the default. And so the what she got back was like, you know, a painting by someone named white or so of like white mountains or, you know, objects that were the color white.
Speaker: And she said, well, that's goes to the very heart of what we mean when we say white supremacy, that it is the default condition we assume. And then blackness is ah something that needs to be acknowledged as different from the default.
Speaker: And that's almost, that's such an interesting example because it's not something extra in the data, it's actually something that's missing, but that reveals a lot about the culture in which those data were created.
Speaker: Right. Yeah. I mean, the same holds true. You see this and in lots of places, right? The same holds true in data on say sexual orientation. mean, lot of the ways that data on sexual orientation ah are collected is ah like, you know, one option is like straight.
Speaker: And then then, and then the next option will be gay comma, not straight where it's like, it's, it's, it's defined as the, as you know, the opposite of whatever that norm or default is in the front. Yeah. Yeah. Yeah.
Speaker: um So with all this in mind, your work now... um seems to be more about like actually getting into ah communities and working with people.
Speaker: um We first started talking a few months ago about your um your map room project. um Maybe that's a good starting point. um And we can talk a little bit about you know what that project is and then and then how you've engaged people ah to work ah you know, sort of, I guess very simply to create a data visualization. I mean, they may not know that they're creating a data visualization, but that's what they are doing. um So, so yeah, so maybe we could start there and and or or maybe we start here, like talk a little bit about your lab at university city yeah and then we can talk about some specific projects.
Speaker: Yeah. Yeah. So ah I've had a lab for for, quite a while called the local data design lab. And actually I had a number of wonderful students who, um you know, passed through there and helped me with pieces of the book.
Speaker: People like Peter Pollack and his brother, Chris Pollack, actually, two great brothers. And, but more recently i have really tried kind of reoriented my work towards this larger collaborative center that i helped to found in the last couple of years is called the Interdisciplinary um Media Arts Center.
Speaker: And the idea is to really bring people together from across Georgia Tech, from different disciplines in order to think about creative approaches to research, um ways of bringing art and design making um expressive activities into projects, into serious scientific and technical projects as a way of broadening their accessibility, um doing community engagement work, and just putting whatever scientific problems there are in this in this broader material you know social and material context.
Speaker: And I think one of the, one of the first successes we had actually before the center, but in way inspired the center was using the map room in a huge, um, project about sea level rise in Savannah, Georgia.
Speaker: And in Savannah, Georgia, as many people know, they get hit by a lot of hurricanes. Um, and it's already a very low lying area.
Speaker: And if, you know, a hurricane coincides with a king tide, you know, there's often substantial flooding and ah and it's projected that, you know, depending on sea level rise, much of the city could be underwater in the next hundred years.
Speaker: Yeah. and Scientists and engineers at Georgia Tech have been working for a couple of years to try and record some of these changes by putting sensors on things like piers and bridges, people like Russ Clark.
Speaker: um and um um a host of of scientists, Kim Cobb and and others, who's a climate scientist. Anyway, they so they had all this data.
Speaker: And in in a way it was like meant to be very local. Because here is, you know, we think of climate change as being like a global problem, but here, you know, these people were collecting data on like, where is the sea level at this pier, at this bridge, and how does it affect this local community?
Speaker: yeah And they were having a trouble though, engaging the community in thinking about these problems. And, you know, they had some of the usual kind of oh, we're going to have like ah a kind of open house or we're going to have a public meeting and we'll talk about the data and why it matters, we're going to build a data dashboard where anyone can go look at the data.
Speaker: And my sense is that they were somewhat unsatisfied with the response they'd gotten. And so I... introduce them to this map room technology I've been developing actually with ah with Jair Thorpe originally.
Speaker: um Thorpe is a data artist who works out of new York City. he used to run a studio called OCR, the Office for Creative Research. And he led a project called the St. Louis map room in 2017.
Speaker: And so a lot of the original framing and kind of some of the tech ideas came from that. And, you know, I basically reached out to him and said, ah you know, is this something you'd want to try in other cities and could this become a research project?
Speaker: Anyway, so we, you know, brought this to to Savannah and people were immediately and enthralled with it. I mean, it was startling to me. I think kids, you know, we took it to a local high school, kids were drawing, you know, so the idea with the map room technology is it's quite simple, actually. It's a, a overhead projector that's driven by software we've written using open street maps and it projects like the street grid onto a table.
Speaker: And then you can lay down paper or canvas and you have this, the projection guides you in making map, making, as you say, a data visualization.
Speaker: right And the fun thing is, so The projection operates kind of as a, as I said, guideline or suggestion, but then people have to draw, people follow the lines of the projection and draw the parts of the city they wanna draw that are meaningful to them.
Speaker: And because their drawings have followed these lines from open street maps um and they're geographically precise to a certain extent, then you can overlay any data, existing data you want on top of that. So you can say, okay, you've drawn your route to school.
Speaker: Let's look at he you know where flooding has happened in the past 20 years. right um And so that creates a dialogue between their own experience and what are recorded in the data.
Speaker: And I think what's really exciting about that is it's a kind of two-way thing where on one hand, there may be stuff that's invisible to them. You know, look they they may say, oh, I didn't realize, you know, there had been so much flooding. Maybe they just moved to Savannah last year and they're like, I didn't realize this street flooded, you know, every couple of years. yeah um Or they may,
Speaker: see an error in the data because they actually know quite a bit about the place they live and they know things that aren't in the data. So they may say, actually, this is wrong or the flooding kind of extended beyond this, or I live right here in my basement flooded.
Speaker: And, um, because you know, that flooding map, you know, was yeah. Created using certain kinds of indicators and so forth and it's going to be partial as i said and limited yeah um and then what i find is in you know as people start making these maps and they're huge maps you know one version they were like 10 feet by 10 feet other versions they've been longer and thinner but we you know we're all around a table and we start talking and some of the most interesting stuff that happens is not on the map but
Speaker: what passes between people and how they talk about the place they live and its past and also what they, they hope for in the future. And it became a very concrete way of taking the principles of all data are local, the book and putting them into practice saying it matters where, where data are made, but also where you are when you're using data.
Speaker: Yeah. Um, and, uh, Yeah, I could talk a lot more about it, but it was it was a wonderful project. It's still ongoing. we ended up getting a big grant from National Science Foundation through the Civic Innovation Challenge together with um People like Alan Hyde, who's a sociologist at Georgia Tech, and ah couple of other um people from around Tech. And we also collaborated with um um a university in in Savannah and the Savannah public school system. And so it was a very rich opportunity to kind of see how this creative activity could
Speaker: really add to, in a substantive way, add to this scientific project in a way that NSF could get. Right, right, right. But it it also strikes me that it empowers people with their own data, their own experiences in ways that maybe...
Speaker: your sort of standard community meeting, even if they're allowed to get up and share ah their story at a lectern or something like that. But but the the act of drawing, the act of standing around the map empowers them in in ways that some of these other ways just maybe they just fundamentally don't.
Speaker: Yeah, yeah. I think one of the most interesting things is it allows people to disagree about their representations of the city. One of the most wonderful things about it is because you have multiple people working on the map at the same time, they might not necessarily be in alignment with how they're drawing. And these i remember these two kids, middle school age kids at a Savannah public school, were starting to draw the Savannah River from opposite ends.
Speaker: And on one side, it's this crystalline blue surface Yeah. Like pristine. And on the other side, it's like orange and brown with like debris in it. And they meet at the center and there's really like, you know, this line at the center where they met.
Speaker: And of course they had this discussion about how one kid thought the river was this beautiful natural resource. And the other one saw it as heavily polluted and a danger really. Yeah.
Speaker: and And then, you know, they could ask, okay, well, are there data that exists that could help us learn about this? And there might not necessarily be the data out there, but what I like about is it's starting from their experience rather than starting from the data.
Speaker: Right. So I always try to get them to start from what is your experience and then asking, Are there data that speak to this or not? Because I think it's important for people to be to be able to say, actually, no, I care about this thing that's not in the data.
Speaker: And maybe that can be the start of a process where they go out and try to collect some data or encourage the city to collect it or partner with scientists. And that actually happened in the sea level sensors project because a number of people would come to the mapping sessions and they would say,
Speaker: you know, what I'm really worried about, i remember this one woman was like, why is it that when I come out of my house in the morning, there's like white particulate matter all over my car and what is going on with the air quality?
Speaker: And that was not at all what we expected to talk about, but because of the openness of this system, yeah sometimes I call these systems open data settings, you know, they're, they're places in which Anything can become data, you know, your story can become data um ah rather than this kind of more close scenario where it's like, these are the data we have. What can we say about them anyway? So she started to draw like, here's where I've also, you know, smelled, you know, problematic smells and, you know, eventually we realize, okay, there's like a paper mill that's nearby and that's causing a lot of pollution.
Speaker: um and And then, um you know, within a year, this Russ Clark and and some other engineers started to get some simple air quality sensors and put them, you know, in a neighborhood in Savannah. So I think it shows how.
Speaker: Yeah, that there is a kind of bi-directionality yeah that opens up. Yeah, I also wonder about the data that, or the parts of cities that that participants don't draw.
Speaker: like So you've got this projection of some area of the city on this big or small piece of paper, yeah people are drawing on it, and then have you seen times where people talk about what they haven't drawn?
Speaker: Because I feel like what's missing is also, is some can be like equally as interesting what people have drawn. Yeah, yeah. There's actually an interesting project going on right now by one of my PhD students, Mohsen Yousufi, that really stemmed from something we heard from a number of middle school age kids in in this Savannah project.
Speaker: And it was, we'd say like, we'd ask them to draw like a place that they frequently go in the city or something like that. And they'd say, well, I don't really know anything about the city.
Speaker: Yeah. um And so I can't, you know, i can't draw, i don't know what to draw. And, you know, we struggled with that early on, like quite a bit. It's like, what is going on here? And,
Speaker: And he has framed it in this interesting way as a, what he calls an epistemic obstacle. um um And because it raises this question, is it really true that they don't know anything or do they think that what they know is not important?
Speaker: um Or yeah, is there something else going on? Maybe they think they, they're not sure if what they know is true or ah yeah. So there are all these,
Speaker: other explanations. And I think what's so interesting for us about the map room and its various incarnations is it becomes a place in which to study how people relate to data and some of the ah problems and that come up in limiting them sometimes, you know, from ah engaging with data in certain ways or um becoming fearful. you know, one kid said at some point, is like, do we really have to talk about this? Because it's scary.
Speaker: oh You know, it's scary to me to think about my neighborhood being affected by ah hurricane or flooding. um And I think it's important for us to to see that, to hear that, and to really take in that data, for example, are not just rational, logical elements, but they carry this emotional weight.
Speaker: yeah And I think that really differentiates, let's say, how humans deal with data and how computers deal with data. you know For humans, data are a felt experience, both like perceptually, you know, we see the data.
Speaker: um It's a sensory experience, but also we have to manage our perception of the data with our own complex feelings about the subjects they represent.
Speaker: And computers and, you know, we could talk about in terms of ai doesn't have that. And in you know, one could say, oh, well that's the advantage cause AI can be impartial, but you know, I think those emotions matter because emotions are what motivate us to take action and do things.
Speaker: And so we don't want people to be just objective and impartial. we want them to be motivated. Obviously we don't want them to be biased.
Speaker: But I think it's in the map room, you can see that people have a position. They have a standpoint, you know, um in the language of, you know, standpoint epistemology, we could say, you know, right um and and and we can start to see that and how it plays out in the way that different people draw or don't draw parts of the city. Right, right.
Speaker: um I want to close up with one last question. I'm thinking about folks who you know who listen to the show, who do their day-to-day work, right? They collect some data or they download some data and they analyze the data. They make a graph or a dashboard whatever they're doing.
Speaker: and I'm curious what you would tell that person who works on big data sets and we can, yeah big could mean anything in this, in this, in this context, but what you would say or recommend to people to have this local sort of perspective and as they're working on their various projects.
Speaker: Yeah. It's such an important question. the the last line of the book actually goes something like this. ah Don't take the existence or the accessibility of data as permission to remain at a distance.
Speaker: Use it as a starting point to get closer to, to learn more about the people and the places that the data represent. And i i really think this has been at the root of everything that's enabled me to write this book and do these projects.
Speaker: At the end of the day, I'm not a domain expert in any of these areas where I use data, but I'm able to do the work because I talk to people who have either created the data or they um they use the data every day and they really know it, or they maybe are in the data, they're subjects of the data, but they are able to connect me to this broader system of knowledge. Uh, and I like to really think about sometimes I say, well, data are like the index of a book, you know, the index of a book is great. It's really useful for understanding what's in the book.
Speaker: And, uh, you know, can can tell you so much, but they're much more useful when you use the index with the book. And so, yeah you know, in a way they become pointers. And I often ask my students to create what I call data guides um before they start working with a data set.
Speaker: And that means, you know, being intentional about going out and having a discussion with somebody who knows this data source, um looking at how are other people making arguments or visualizations with the same data? How are these data created differently in other places?
Speaker: um What's an example of like an anomaly in the data? um And so they are starting from a more engaged position and it is very hard for them, frankly. I just went through this with some students.
Speaker: Often they come back initially and, you know, They were embarrassed to call someone or they called somebody or reached out over email and they didn't hear back. So they gave up, you know, a lot of people who work with data. um i mean, a lot of us in general, these days, you know, turn to data because it seems easier than dealing with the messiness of, can I trust this person and what do they know and not know, or what is their perspective?
Speaker: But I think it would be a mistake. just from all the different data sources I've worked with, as soon as I talk to somebody about it, it opens up a whole new set of questions and,
Speaker: often i was looking at the wrong thing or I was misinterpreting something. um so it is really powerful ah to have, to build those relationships and see data work, whether it's building visualizations or analysis as a social process isn and not as a either automated process or, or something you do on your own. Right. um In the sense of like mining, you know, the data.
Speaker: because the answers aren't all in there. The answers, there may be pointers to great answers, but their data are always incomplete in a sense.
Speaker: yeah They're there limited because, you know, the whole idea of creating data is to efficiently represent a very complex phenomenon.
Speaker: so You know, that's the advantage that they are smaller than the original phenomenon, but that comes with certain problems. yes And I think most good scientists and researchers know this.
Speaker: The problem is when we have open data and a much broader set of people, including people who have nothing to do with the domain or have not been involved in collecting the data and so forth, expect that they can use the data without any of that context that you run into a problem.
Speaker: Yeah. I mean, I think part of the problem is, as you as you mentioned with some of your students, like this apprehension, fear, what i have you, of talking to people, of reaching out to people and figuring out how to do that and, you know having those conversations is is is hard for lots of people, especially for people who are are more comfortable and like to sit behind the computer and, you know, write the code. and yeah Well, hopefully we're modeling for people why it's so great to have discussions. Yeah, exactly. Exactly.
Speaker: Yanni, thanks so much for coming on the show. So the book is all data are local. The project we talked about is the is the the mapping project, but there's lots of other projects on your site and I'll make sure to put the link on the show notes.
Speaker: And I should mention the book open access, so you can get it free online. Yeah, or you get the lovely hardcover version, which I have already. Yanni, thanks so much for coming on the show. This was a lot of fun. Thank you.
Speaker: Thanks for checking out the show, everybody. i hope you enjoyed that. I hope you'll check out Yanni's book, All Data Are Local. I hope you'll check out his website to learn more about the Mapping Room project. And I hope, of course, you will check out PolicyViz to learn more about data and data visualization.
Speaker: So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
